Short-term traffic flow prediction model based on extreme learning machine with fractional-order optimization
LI Zhuo-xuan
HE Gui-zhong
WEI Yi-heng
CAO Jin-de
Abstract：In intelligent transportation systems,traffic flow prediction plays a key role in traffic management and con-gestion reduction.The current manuscript aims to develop an efficient,non-iterative neural network method for short-term prediction of traffic flow,which is called improved fractional order extreme learning machine(IFra-ELM).Through the fractional ridge regression algorithm,the ELM algorithm has better performance in solving the output weight.This method is called a fractional extreme learning machine(Fra-ELM).An improved fractional-order accumulation layer is added after the input layer to splice it with the original input,which enhances the feature extraction capability of the ELM algorithm.By combining the improved fractional-order cumulative layer with the fractional extreme learning machine,the model's prediction ability and robustness for traffic flow in different scenarios are enhanced.Experimental results show that com-pared with traditional methods,the IFra-ELM model has superior performance in traffic flow prediction.This framework has the potential to improve the accuracy and efficiency of traffic prediction systems and contribute to the development of smart transportation infrastructure.
Keywords：traffic flow predictionneural networksfractional-order accumulationimproved fractional-order extreme learning machine
Publication Date：2025-06-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 1191-1199 )